Case Study > Commercial View

Building a market-leading property platform in Australia

One of Australia’s largest commercial  property portals

Real Estate

Australia

Building a scalable, data-driven & intelligent platform

A leading online marketplace in Australia for buying, selling, and leasing commercial real estate, Commercial View connects agencies and consumers across office spaces, retail outlets, warehouses, and industrial assets. As one of the country’s largest commercial property portals, it is built on a strong foundation of data accuracy, reliability, and market transparency—empowering businesses, investors, and property seekers to make informed decisions with confidence.

Commercial View required a robust and scalable digital ecosystem capable of handling continuous high-volume data ingestion, delivering advanced search intelligence, and ensuring seamless user experiences at scale. To meet these demands and maintain industry-leading standards, Commercial View partnered with Atharva to architect and deliver a high-performance, future-ready, data-driven platform tailored to its unique business needs.

Challenges faced by Our Client

Managing and processing thousands of property records from multiple agencies required a highly scalable and resilient data ingestion framework capable of real-time synchronization without compromising data integrity.

Commercial property discovery involves nuanced and multi-layered criteria. Delivering precise, relevant, and context-aware search results demanded the development of an advanced search architecture.

Maintaining a consistently high level of data accuracy across rapidly changing listings posed a significant operational and technical challenge, especially at scale.

Capturing, processing, and interpreting user interaction data to derive meaningful insights required a robust analytics foundation.

Ensuring long-term platform stability and performance while adopting the latest advancements in Ruby and Rails required carefully planned and executed upgrade cycles.

Supporting large-scale data processing, concurrent users, and high-performance search capabilities necessitated a cloud-native, distributed infrastructure.

Unified Data Aggregation Framework

Engineered a centralized system to seamlessly ingest, normalize, and manage large-scale property data from multiple agency sources, ensuring consistency and accessibility.

Intelligent Search & Matching Engine

Developed a sophisticated search algorithm leveraging structured data and user intent to deliver highly relevant and personalized property recommendations.

Automated Data Validation & Daily Synchronization

Implemented automated pipelines to verify, cleanse and standardize property data daily, ensuring reliability and real-time accuracy.

Advanced User Analytics Layer

Integrated comprehensive tracking and analytics capabilities to monitor user behavior, enabling continuous optimization of the platform experience.

Structured Version Upgrade Strategy

Established a disciplined upgrade framework for Ruby and Rails, ensuring alignment with modern standards while minimizing disruption through rigorous testing and phased deployment.

Cloud-Native, Scalable Architecture

Deployed the platform on Heroku with load balancing, distributed background processing, and Solr-powered indexing to ensure high availability, scalability, and performance.